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All Journal Journal of Information Systems and Informatics
Chyquitha Danuputri
Muhammadiyah University of Makassar
Computer Science & IT

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Data Mining Analysis for KIP Scholarship Eligibility Using Integrated DBSCAN and TOPSIS Imam Akbar; Chyquitha Danuputri; Rahma; Ita Sarmita Samad
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1534

Abstract

This study aims to objectively analyze the feasibility of prospective recipients of the Smart Indonesia Card Scholarship (KIP-K) by integrating the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The research dataset consists of 287 data on prospective scholarship recipients with 11 main attributes that reflect the socio-economic and academic conditions of students. The research process includes data collection, pre-processing, transformation of categorical attributes into numerical values using a linear weighting scheme, cluster analysis using DBSCAN, and candidate ranking using TOPSIS. DBSCAN is used to identify cluster patterns and detect anomalies in the data of potential recipients, while TOPSIS is used to rank candidates based on proximity to the ideal solution. The results of the grouping produced 10 clusters and one noise cluster that showed a variety of socio-economic characteristics of prospective scholarship recipients. The results of the ranking show that some of the candidates with the highest TOPSIS scores come from clusters with higher levels of economic vulnerability. In addition, some of the high-scoring candidates also came from the noise cluster, indicating that even though they did not belong to a particular group, they still met the eligibility criteria based on a multi-criteria evaluation. These findings show that the combination of DBSCAN and TOPSIS has the potential to support the process of analyzing the eligibility of scholarship recipients in a more systematic and data-driven manner.
Enhancing YOLOv12-Based Rice Leaf Disease Detection through Evaluation of Three Data-Split Scenarios Ida Mulyadi; Fahrim Irhamna; Chyquitha Danuputri; Ridwang; Ridha Awalia
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1580

Abstract

One of the most significant staple crops in the world is rice, and one of the main causes of the drop in agricultural yields is illnesses that affect rice leaves. To avoid large agricultural losses, early diagnosis of these illnesses is essential. The goal of this project is to use YOLOv12, the most recent deep learning-based object detection architecture, to create a rice leaf disease detection system. The model was trained using a dataset of 4,744 photos of rice leaves that included three disease classes: Leaf Blast, Brown Spot, and Bacterial Leaf Blight. Methods to boost variability and enhance detection performance, image preprocessing with data augmentation was used. Standard object detection criteria, such as mean Average Precision (mAP), precision, and recall, were used to assess the model. The YOLOv12 model was highly effective in detecting rice leaf illnesses. According to the experimental data, it achieved a mAP of 97%, a precision of 96%, and a recall of 96.5%. The use of YOLOv12's greater efficiency and quality in detecting small objects—which is essential for identifying illness symptoms on leaves—is what makes this study successful. These results lay the groundwork for upcoming precision agricultural real-time monitoring applications.
Co-Authors Fahrim Irhamna Ida Mulyadi Imam Akbar Ita Sarmita Samad Rahma Ridha Awalia Ridwang